On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models - Makatjane, Katleho; Moroke, Ntebogang; Xaba, Diteboho (Journal of Social Economics Research, 2018)
The aim of this study is to build an early warning system (EWS) model for inflation rates of South Africa (SA). A logistic regression model (LRM) is used in collaboration with a Markov-switching Bayesian vector autoregressive (MS-BVAR) to produce the estimates. Monte Carlo experimental methods are used to simulate both the inflation rate and repo rate of the SA economy. The procedure simulated 228 observations for the period of January 1999 to December 2017. Preliminary results confirmed the applicability of both models for further analyses. MS (2)-BVAR(1) proved to be the most appropriate model for detecting regime shifts in inflation rates. The results indicate that SA inflation might be in a low inflation regime for the period of 11 years and 4 months. Surprisingly, we discovered that the repo rate is not a good tool to combat the inflation rate in SA i.e a 1% increase in the repo in a month significantly increased inflation rate by about 81%. The findings also confirmed 51% and 53% of in-sample and out-of-sample SA inflation crises forecasts to be correctly classified. This is in accordance with the reported results of Cruz and Mapa (2013) and Makatjane and Xaba (2016). The verdicts of the study are relevant for policy purposes and literature.